{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train : (768, 9)\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "path = 'D://E/';\n",
    "train = pd.read_csv('D://E/pima-indians-diabetes.csv')\n",
    "train.head()\n",
    "print('Train :',train.shape)\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#有些数据存在缺失，有些0作为数值指标来说，是没有意义的。下列变量的值为0时，没有意义\n",
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "print((train[NaN_col_names] == 0).sum())#统计各个特征值为0的个数\n",
    "#3，4为0的数量多，1 2 5为0数量少。所以不同的列，给不同的判断策略"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of occurrences')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(train['Target'])\n",
    "plt.xlabel('Diabetes')\n",
    "plt.ylabel('number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of occurrences')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAHOtJREFUeJzt3XuYHVWZ7/Hvj4RLEi4hJmAmFwMa8cJhIPRBzoA8HIIIDBAwIDKAQdAcuSOHIzD4iKMyR7yAo6NgMEBEDHdMYLgkE8lB1ABJIBcMNyFAQyBBISCMQOA9f9Rq2DSV7updu3rvdH6f59nPrqpdterd3b373bVWrbUUEZiZmXW2QbMDMDOz1uQEYWZmuZwgzMwslxOEmZnlcoIwM7NcThBmZpbLCcLMzHI5QZiZWa7KEoSkSyWtlLQ057UzJIWkoWldkn4k6VFJiyWNqyouMzMrpn+FZV8O/Dvwi9qNkkYBnwKerNm8HzA2PT4BXJSeuzR06NAYM2ZMY6I1M1tPLFiw4PmIGNbdfpUliIi4U9KYnJcuBL4KzKjZNgH4RWTjfsyTNFjS8IhY0dU5xowZw/z58xsVspnZekHSE0X269U2CEkHAU9HxKJOL40AnqpZb0/bzMysSaqsYnoXSQOBc4B98l7O2ZY7iqCkycBkgNGjRzcsPjMze7fevIL4ILANsEjScmAksFDS+8muGEbV7DsSeCavkIiYEhFtEdE2bFi3VWhmZlanXksQEbEkIraKiDERMYYsKYyLiGeBmcDn091MuwKru2t/MDOzalV5m+t04A/AdpLaJR3Xxe63AI8BjwKXACdUFZeZmRVT5V1MR3Tz+pia5QBOrCoWMzPrOfekNjOzXE4QZmaWywnCzMxy9Vo/iHXJip+eU+r44Sec16BIzMyax1cQZmaWywnCzMxyOUGYmVkuJwgzM8vlBGFmZrmcIMzMLJcThJmZ5XKCMDOzXE4QZmaWywnCzMxyOUGYmVkuJwgzM8vlBGFmZrmcIMzMLJcThJmZ5XKCMDOzXE4QZmaWywnCzMxyVZYgJF0qaaWkpTXbvifpQUmLJd0oaXDNa2dLelTSQ5I+XVVcZmZWTJVXEJcD+3baNhvYPiJ2AB4GzgaQ9DHgc8DH0zE/ldSvwtjMzKwblSWIiLgT+EunbbMiYk1anQeMTMsTgKsi4rWIeBx4FNilqtjMzKx7zWyDOBa4NS2PAJ6qea09bTMzsyZpSoKQdA6wBriyY1PObrGWYydLmi9p/qpVq6oK0cxsvdfrCULSJOAA4MiI6EgC7cComt1GAs/kHR8RUyKiLSLahg0bVm2wZmbrsV5NEJL2Bc4EDoqIV2temgl8TtLGkrYBxgL39GZsZmb2bv2rKljSdGBPYKikduBcsruWNgZmSwKYFxFfjogHJF0D/JGs6unEiHizqtjMzKx7lSWIiDgiZ/PULvY/DzivqnjMzKxn3JPazMxyOUGYmVkuJwgzM8vlBGFmZrmcIMzMLJcThJmZ5XKCMDOzXE4QZmaWywnCzMxyVdaTujetuuiXpY4fdvxRDYrEzKzv8BWEmZnlcoIwM7NcThBmZparRwlC0paSdqgqGDMzax3dJghJcyVtLmkIsAi4TNIF1YdmZmbNVOQKYouIeAn4DHBZROwM7F1tWGZm1mxFEkR/ScOBzwI3VxyPmZm1iCIJ4pvA7cCfIuJeSdsCj1QblpmZNVu3HeUi4lrg2pr1x4CJVQZlZmbNV6SR+sOS5khamtZ3kPS16kMzM7NmKlLFdAlwNvAGQEQsBj5XZVBmZtZ8RcZiGhgR90iq3bamonj6pPsuPrDuY3f68k0NjMTMrLgiVxDPS/ogEACSDgVWdHeQpEslreyomkrbhkiaLemR9Lxl2i5JP5L0qKTFksbV+X7MzKxBiiSIE4GfAR+R9DRwGnB8geMuB/bttO0sYE5EjAXmpHWA/YCx6TEZuKhA+WZmVqEidzE9BuwtaRCwQUS8XKTgiLhT0phOmycAe6blacBc4My0/RcREcA8SYMlDY+Ibq9UzMysGkXuYvpXSYMj4pWIeDmNx/TtOs+3dcc//fS8Vdo+AniqZr/2tC0vnsmS5kuav2rVqjrDMDOz7hSpYtovIl7sWImIF4D9GxyHcrZF3o4RMSUi2iKibdiwYQ0Ow8zMOhRJEP0kbdyxImkAsHEX+3fluTRsB+l5ZdreDoyq2W8k8Eyd5zAzswYokiB+CcyRdJykY4HZZO0H9ZgJTErLk4AZNds/n+5m2hVY7fYHM7PmKtJI/V1JS4DxZFVB34qI27s7TtJ0sgbpoZLagXOB7wDXSDoOeBI4LO1+C1m11aPAq8AXev5WzMyskYp0lCMibgVu7UnBEXHEWl4an7NvkN1Oa2ZmLaLIXUyfSR3bVkt6SdLLkl7qjeDMzKx5ilxBfBc4MCKWVR2MmZm1jiKN1M85OZiZrX+KXEHMl3Q18GvgtY6NEXFDZVGZmVnTFUkQm5PdWbRPzbYAnCDMzPqwIre5+pZTM7P1kGeUMzOzXJ5RzszMchVJEAMj4p5O2zyjnJlZH1fZjHJmZrZuK3IX04nAFN6ZUe5x4MhKozIzs6brMkFI2gBoi4gezyhnZmbrti6rmCLiLeCktPyKk4OZ2fqjSBvEbElnSBolaUjHo/LIzMysqYq0QRybnmuH4w5g28aHY2ZmraJIG8RREfG7XorHzMxaRJE2iO/3UixmZtZCirRBzJI0UZIqj8bMzFpGkTaI04FBwBpJfyOblzoiYvNKIzMzs6YqMprrZr0RiJmZtZZuE4SkPfK2R8SdjQ/HzMxaRZEqpv9Ts7wJsAuwANir3pNK+grwRbLbZZcAXwCGA1cBQ4CFwNER8Xq95zAzs3K6baSOiANrHp8Ctgeeq/eEkkYAp5AN4bE90I9s+PDzgQsjYizwAnBcvecwM7PyitzF1Fk7WZIooz8wQFJ/YCDZ6LB7Adel16cBB5c8h5mZlVCkDeLHpKG+yRLKjsCiek8YEU9L+j7wJPBfwCyyKqsXI6Jjnol2YES95zAzs/KKtEHMr1leA0wv07Na0pbABGAb4EXgWmC/nF0jZxuSJgOTAUaPHl1vGGZm1o0iCeI64G8R8SaApH6SBkbEq3Wec2/g8YhYlcq7AfgHYLCk/ukqYiTwTN7BETGFbH4K2tracpOImZmVV6QNYg4woGZ9APCfJc75JLCrpIGpd/Z44I/AHcChaZ9JwIwS5zAzs5KKJIhNIuKvHStpeWC9J4yIu8muShaS3eK6AdkVwZnA6ZIeBd4HTK33HGZmVl6RKqZXJI2LiIUAknYma1yuW0ScC5zbafNjZH0szMysBRRJEKcB10rqaBMYDhxeXUhmZtYKiozFdK+kjwDbkQ3U92BEvFF5ZGZm1lTdtkFIOhEYFBFLI2IJsKmkE6oPzczMmqlIFdOXIuInHSsR8YKkLwE/rS4sW5tbpu5f6vj9j7ulQZGYWV9X5C6mDWonC5LUD9ioupDMzKwVFLmCuB24RtLFZL2bvwzcVmlUZmbWdEUSxJnA/wKOJ2ukngX8vMqgzMys+YrcxfSWpKnAXWRXEA91DLthZmZ9V5HRXPckG357OdkVxChJkzyjnJlZ31akiukHwD4R8RCApA8D04GdqwzMzMyaq8hdTBt2JAeAiHgY2LC6kMzMrBUUmg8itUFckdaPJJvgx8zM+rAiCeJ44ESyeaQF3Ik7yZmZ9XlF7mJ6DbggPayPuXTaPqWOP3bSrAZFYmatpkgbhJmZrYecIMzMLNdaE4SkK9Lzqb0XjpmZtYquriB2lvQB4FhJW0oaUvvorQDNzKw5umqkvphsUL5tyW5rVc1rkbabmVkftdYriIj4UUR8FLg0IraNiG1qHk4OZmZ9XJHbXI+X9PfAJ9OmOyNicbVhmZlZsxWZcvQU4Epgq/S4UtLJVQdmZmbNVaQn9ReBT0TEKwCSzgf+APy43pNKGkw2p8T2ZO0ZxwIPAVcDY8hGjv1sRLxQ7znMzKycIv0gBNTO//Am726wrse/AbdFxEeAvweWAWcBcyJiLDAnrZuZWZMUuYK4DLhb0o1p/WBgar0nlLQ5sAdwDEBEvA68LmkCsGfabRowl2w2OzMza4IijdQXSJoL7E525fCFiLivxDm3BVYBl6XG7wXAqcDWEbEinXOFpK1KnMPMzEoqcgVBRCwEFjbwnOOAkyPibkn/Rg+qkyRNBiYDjB49ukEhmZlZZ80Yi6kdaI+Iu9P6dWQJ4zlJwwHS88q8gyNiSkS0RUTbsGHDeiVgM7P1Ua8niIh4FnhK0nZp03jgj8BMYFLaNgmY0duxmZnZO7qsYpLUD7g9IvZu8HlPJutPsRHwGPAFsmR1jaTjgCeBwxp8TjMz64EuE0REvCnpVUlbRMTqRp00Iu4H2nJeGt+oc1hzfPvqT9d97NcOv72BkZhZWUUaqf8GLJE0G3ilY2NEnFJZVGZm1nRFEsR/pIeZma1HivSDmCZpADA6Ih7qhZjMzKwFFBms70DgfrK5IZC0o6SZVQdmZmbNVeQ2128AuwAvwtsNzNtUGJOZmbWAIgliTc4dTFFFMGZm1jqKNFIvlfRPQD9JY4FTgN9XG5aZmTVbkSuIk4GPA68B04GXgNOqDMrMzJqvyF1MrwLnpImCIiJerj4sMzNrtiJ3Mf13SUuAxWQd5hZJ2rn60MzMrJmKtEFMBU6IiN8CSNqdbBKhHaoMzGy/GUeUOv7WCdMbFInZ+qlIG8TLHckBICLuAlzNZGbWx631CkLSuLR4j6SfkTVQB3A42XSgZmbWh3VVxfSDTuvn1iy7H4SZWR+31gQREf+zNwMxM7PW0m0jtaTBwOeBMbX7e7hvM7O+rchdTLcA84AlwFvVhmNmZq2iSILYJCJOrzwSMzNrKUUSxBWSvgTcTDbcBgAR8ZfKojKrwP43nl/q+FsOObNBkZitG4okiNeB7wHn8M7dSwFsW1VQZmbWfEUSxOnAhyLi+aqDMTOz1lGkJ/UDwKtVB2JmZq2lyBXEm8D9ku7g3W0QpW5zldQPmA88HREHSNoGuAoYAiwEjo6I18ucw8zM6lfkCuLXwHlkkwQtqHmUdSqwrGb9fODCiBgLvAAc14BzmJlZnYrMBzGt0SeVNBL4R7LEc7okAXsB/5R2mUY2F/ZFjT63mZkVU6Qn9ePkjL0UEWXuYvoh8FVgs7T+PuDFiFiT1tuBEWuJZzIwGWD06NElQjAzs64UaYNoq1neBDiMrJ2gLpIOAFZGxAJJe3Zsztk1d0DAiJgCTAFoa2vzoIFmZhUpUsX0506bfijpLuDrdZ5zN+AgSfuTJZzNya4oBkvqn64iRgLP1Fm+mZk1QJEqpnE1qxuQXVFstpbduxURZwNnp7L3BM6IiCMlXQscSnYn0yRgRr3nMDOz8opUMdXOC7EGWA58toJYzgSukvRt4D6yqU7NWtYB19f/J3rzRN+kZ62vSBVTZfNCRMRc0ux0EfEYsEtV5zIzs54pUsW0MTCR984H8c3qwjIzs2YrUsU0A1hN1jnutW72NTOzPqJIghgZEftWHomZmbWUIkNt/F7Sf6s8EjMzaylFriB2B45JPapfI+vUFhGxQ6WRmZlZUxVJEPtVHoWZmbWcIre5PtEbgZiZWWsp0gZhZmbrIScIMzPL5QRhZma5nCDMzCyXE4SZmeVygjAzs1xF+kGYWcUOvO76UsffdOjEBkVi9g5fQZiZWS4nCDMzy+UEYWZmuZwgzMwslxOEmZnl8l1MZn3QIdffUer4GydWNhW9rUN8BWFmZrl6PUFIGiXpDknLJD0g6dS0fYik2ZIeSc9b9nZsZmb2jmZcQawB/ndEfBTYFThR0seAs4A5ETEWmJPWzcysSXo9QUTEiohYmJZfBpYBI4AJwLS02zTg4N6OzczM3tHUNghJY4CdgLuBrSNiBWRJBNiqeZGZmVnTEoSkTYHrgdMi4qUeHDdZ0nxJ81etWlVdgGZm67mmJAhJG5Ilhysj4oa0+TlJw9Prw4GVecdGxJSIaIuItmHDhvVOwGZm66Fm3MUkYCqwLCIuqHlpJjApLU8CZvR2bGZm9o5mdJTbDTgaWCLp/rTtn4HvANdIOg54EjisCbGZmVnS6wkiIu4CtJaXx/dmLGZmtnbuSW1mZrmcIMzMLJcThJmZ5XKCMDOzXE4QZmaWywnCzMxyOUGYmVkuJwgzM8vlBGFmZrk8J7WZdevw6x8udfzVEz/coEisNzlBmNk67TdXlhv2f68jPSr02riKyczMcvkKwsx61ZQbcqd6KWzyZzzZZG/xFYSZmeVygjAzs1xOEGZmlssJwszMcjlBmJlZLicIMzPL5QRhZma5nCDMzCyXE4SZmeVquQQhaV9JD0l6VNJZzY7HzGx91VJDbUjqB/wE+BTQDtwraWZE/LG5kZnZ+uKRf3+u1PFjT9q6QZE0X0slCGAX4NGIeAxA0lXABMAJwszWSc9e8EDdx77/9I+/a33lj+eUimWrk8f3aP9Wq2IaATxVs96etpmZWS9TRDQ7hrdJOgz4dER8Ma0fDewSESfX7DMZmJxWtwMeKlD0UOD5BobayPJaObZWL6+VY2t0ea0cW6PLa+XYGl1es2L7QER0OxFGq1UxtQOjatZHAs/U7hARU4ApPSlU0vyIaCsfXuPLa+XYWr28Vo6t0eW1cmyNLq+VY2t0ea0cG7ReFdO9wFhJ20jaCPgcMLPJMZmZrZda6goiItZIOgm4HegHXBoR9bfwmJlZ3VoqQQBExC3ALQ0utkdVUr1cXivH1urltXJsjS6vlWNrdHmtHFujy2vl2FqrkdrMzFpHq7VBmJlZi+jzCaKRQ3dIulTSSklLGxDXKEl3SFom6QFJp5YsbxNJ90halMr7lwbE2E/SfZJubkBZyyUtkXS/pPkNKG+wpOskPZh+hv+jRFnbpbg6Hi9JOq1EeV9Jv4OlkqZL2qTeslJ5p6ayHqgnrry/W0lDJM2W9Eh63rJEWYel2N6S1KM7aNZS3vfS73WxpBslDS5Z3rdSWfdLmiXp78qUV/PaGZJC0tASsX1D0tM1f3v7l4lN0tU1ZS2XdH/R8nJFRJ99kDV0/wnYFtgIWAR8rER5ewDjgKUNiG04MC4tbwY8XDI2AZum5Q2Bu4FdS8Z4OvAr4OYGvN/lwNAG/m6nAV9MyxsBgxv4N/Ms2X3i9Rw/AngcGJDWrwGOKRHP9sBSYCBZm+F/AmN7WMZ7/m6B7wJnpeWzgPNLlPVRsj5Jc4G2BsS2D9A/LZ9fNLYuytu8ZvkU4OIy5aXto8hupnmi6N/1WmL7BnBGnX8bXf4/An4AfL3ev72I6PNXEG8P3RERrwMdQ3fUJSLuBP7SiMAiYkVELEzLLwPLKNFrPDJ/TasbpkfdDUySRgL/CPy83jKqImlzsg/HVICIeD0iXmxQ8eOBP0XEEyXK6A8MkNSf7B/7M93s35WPAvMi4tWIWAP8P+CQnhSwlr/bCWRJlvR8cL1lRcSyiCjSYbVoebPSewWYR9Yfqkx5L9WsDqIHn4suPvMXAl9tUFl16ao8SQI+C0wvc46+niDWiaE7JI0BdiL71l+mnH7pknIlMDsiypT3Q7IPwFtlYqoRwCxJC1Jv+DK2BVYBl6UqsJ9LGlQ+RCDre1P3hyoinga+DzwJrABWR8SsEvEsBfaQ9D5JA4H9eXdn0nptHRErIPuyAmzVgDKrcCxwa9lCJJ0n6SngSODrJcs6CHg6IhaVjSs5KVWBXVq0qq+ATwLPRcQjZQrp6wlCOdta6rYtSZsC1wOndfqm02MR8WZE7Ej2jWsXSdvXGdMBwMqIWFAmnk52i4hxwH7AiZL2KFFWf7JL64siYifgFbJqklJS58yDgGtLlLEl2bfzbYC/AwZJOqre8iJiGVk1y2zgNrJq0jVdHtRHSDqH7L1eWbasiDgnIkalsk4qEdNA4BxKJpkaFwEfBHYk+0LxgwaVewQlrx6g7yeIbofuaCZJG5Ilhysj4oZGlZuqW+YC+9ZZxG7AQZKWk1XL7SXplyVjeiY9rwRuJKv+q1c70F5zhXQdWcIoaz9gYUSUGe95b+DxiFgVEW8ANwD/UCaoiJgaEeMiYg+yKoVS3wqT5yQNB0jPKxtQZsNImgQcABwZqUK9QX4FTCxx/AfJkv+i9PkYCSyU9P56CouI59IXu7eASyj3uQAgVW1+Bri6bFl9PUG07NAdqY5wKrAsIi5oQHnDOu72kDSA7B/Vg/WUFRFnR8TIiBhD9jP7TUTU/S1Y0iBJm3UskzVC1n0nWEQ8Czwlabu0aTyNGRK+Ed+6ngR2lTQw/Y7Hk7Uv1U3SVul5NNkHv/Q3Q7LPwaS0PAmY0YAyG0LSvsCZwEER8WoDyhtbs3oQdX4uACJiSURsFRFj0uejnexmk2frjG14zeohlPhc1NgbeDAi2kuXVKaFe114kNXZPkx2N9M5JcuaTnYZ+AbZH8ZxJcranay6azFwf3rsX6K8HYD7UnlLKXn3Qk25e1LyLiayNoNF6fFA2d9DKnNHYH56v78GtixZ3kDgz8AWDYjtX8j+CS0FrgA2Llneb8kS4CJgfB3Hv+fvFngfMIfsamQOMKREWYek5deA54DbS8b2KFnbYcfnoid3HeWVd336XSwGbgJGlCmv0+vLKX4XU15sVwBLUmwzgeFlYwMuB75c9u84ItyT2szM8vX1KiYzM6uTE4SZmeVygjAzs1xOEGZmlssJwszMcjlB2HpF0tyejjha53lOUTbKbOlewFWS9M/NjsFalxOEWUGph2pRJ5D1azmyjvNIUm99Np0gbK2cIKzlSBqTvn1fkuYZmJV6h7/rCkDS0DTcAZKOkfRrSTdJelzSSZJOT4P5zZM0pOYUR0n6vbI5FnZJxw9Kg6Xdm46ZUFPutZJuAt4z6F46x9L0OC1tu5isc+BMSV/ptP8xkmZIuk3ZPCXndnrPPwUWAqMk7SPpD5IWphg2Tfvur2y+hLsk/Uhpvg5Ju6T3dV963q7mnDekcz4i6btp+3fIRp29X9KV6WfwH8rmFFkq6fBG/D5tHdaI3nZ++NHIBzCGbJC2HdP6NcBRaXkuac4BYCiwPC0fQ9YDdzNgGLCa1JuUbGjm02qOvyQt70EaSx/415pzDCbrfT8oldtOTk9jYGeyXrCDgE3JeonvlF5bTk4P21TeCrKezAPIevi2pff8FmkOj/Te7gQGpfUzyQaI24Ssl/E2aft0Uk93YHPemUdhb+D6mnM+BmyRjn8CGJVe+2tNbBM7fjZpvXSvcj/W7YevIKxVPR4RHbNhLSD7B9qdOyLi5YhYRZYgbkrbl3Q6fjq8PZ7+5mkMq32As5QNlz6X7B/p6LT/7IjIG3d/d+DGiHglsrk4biAbZrk7syPizxHxX+mY3dP2JyJiXlreFfgY8LsU0yTgA8BHgMci4vHa95JsAVyrbIaxC4GP17w2JyJWR8TfyIbt+EBOXEuAvSWdL+mTEbG6wHuxPqwndapmvem1muU3yb5tQ3Zl0fHFpvNUnrXHvFWz/hbv/lvvPL5MkA0NPzE6TXwj6RNkw4nnyRtOvoi889PpPCJLJEd0imenLsr9FlmSPETZHCNza17r/PN8z2c/Ih6WtDPZ+GX/V9KsiPhmF+ezPs5XELauWU5WtQNwaJ1lHA4gaXeyCX1Wk00feXIagbW7f8Qd7gQOTiO3DiIbtO63BY77lLI5oQeQzeT2u5x95gG7SfpQimegpA+TDQK4bUoAb7+XZAvg6bR8TIE4AN5QNuw8yuZqfjUifkk26VEjhlC3dZivIGxd833gGklHA7+ps4wXJP2erM7+2LTtW2Sz6C1OSWI52XwEaxURCyVdDtyTNv08Iu4rcP67yEbx/BDwq4iYX/MPv6PsVZKOAaZL2jht/lr6ln8CcJuk52vODdk809MknU7xn80Usve8EPgF8D1Jb5GNEHp8wTKsj/Jorma9KP3Tb4uIMrOabRoRf02J7CfAIxFxYaNiNOvgKiazdc+XUsP1A2TVSj9rcjzWR/kKwszMcvkKwszMcjlBmJlZLicIMzPL5QRhZma5nCDMzCyXE4SZmeX6/6aLO82/DAlyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "sns.countplot(train['pregnants'])\n",
    "plt.xlabel('number of pregants')\n",
    "plt.ylabel('number of occurrences')\n",
    "#结果中有怀孕次数很多的情况，显然不符合常理.去掉这些\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26fd25c4358>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='pregnants',hue='Target',data=train)\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'number of occurrences')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plasma\n",
    "fig = plt.figure()\n",
    "sns.distplot(train.Plasma_glucose_concentration,kde = False)\n",
    "plt.xlabel('Plasma_glucose_concentration')\n",
    "plt.ylabel('number of occurrences')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='Target',y='Plasma_glucose_concentration',data=train,hue='Target')\n",
    "plt.xlabel('Diabetes',fontsize=12)\n",
    "plt.ylabel('Plasma_glucose_concentration',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26fd350d470>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#groupby 分组\n",
    "BMIDF = train.groupby(['BMI','Target'])['BMI'].count().unstack('Target').fillna(0)\n",
    "#.fillna(0) 意为将空的值替换为0.\n",
    "#Target值有0，1.\n",
    "BMIDF[[0,1]].plot(kind='bar',stacked=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x26fd3b3a518>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_corr = train.corr().abs()\n",
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr,annot=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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C9/NaCfx5p2oY7pNkHfC9LO7Pu4lJ8kQGYX9VVX109vqqeriqvtaWrweemOToFS5zdk0PtscDwMcY/Lk7bJx/i2l4KXBbVe2fvWI17udm/8xwWHs8MKLPqtrf7UPjnwVeVW0gebYxjqEVU1X7q+qxqvoW8J45allV+xi+nWE/B1wzV5/F7Oe1EvjjTNVwHTBzBcMvAP8w1wG5Etr423uB3VX19jn6fP/M5wxJzmDw7/EfK1fl4+p5apKnzywz+JDuzlndrgNe3a7WOQt4aGZYYsrmPBtabft5yPAxuxX4+Ig+fwe8KMmRbTjiRa1txSV5CfA7wMuq6utz9BnnGFoxsz5fesUctazGqWBeCNxdVXtHrVz0fl6JT6In9Gn2eQyudPk88Lut7U0MDj6A72bw5/we4BbgWVOu96cY/Fl4O7Cr/ZwH/Brwa63PxcBdDK4KuAn4ySnX/KxWy2dbXTP7ebjmMLi5zeeBO4BNq+DYeAqDAP/eobZVtZ8Z/DLaB/wvgzPKCxl8xnQjcG97PKr13QT82dC2v9KO6z3Aa6ZY7x4GY90zx/PMVXHPAK4/1DE0xZr/vB2ntzMI8eNm19yePy5fplVza3/fzPE71HfJ+9lv2kpSJ9bKkI4kaYkMfEnqhIEvSZ0w8CWpEwa+JHXCwJeWWZKNc83SKK0kA1+HvSRPmHIJGxlc5y1NlYGvNS3JhjZH+442QdaHkzylzRV+WZJPA+cnOTnJ37aJpv4pyQ+37U9OclOSzyR5U5KvtfZzkvxje727k1w19G3dy1r/O5NsH2r/xyRvTXJLkn9N8rz2zc03Ab/U5i3/pSQ/PTTf+b/MfGNSWm4Gvg4HPwRsr6ofAx5mcG8EgP+uqp+qqg8yuCfob1TVjwO/Dby79XkH8I6q+gkeP3/KacAlwKkMvtl4dmt/V1X9RFX9KPA9DOaWmbGuqs5o272xBtPtXsbg/gwbq+qa9v4XVdVGBjMifmMyu0E6NANfh4MHquqf2/JfMJjWAtrEU23G0p8EPtTuHvSnDG5QA/BcvjPD5tWzXveWqtpbg4m3dgEbWvsLMrir2h3AzwDD0y3PTJJ361D/2f4ZeHuS3wSOqO/MMS8tq3XTLkCagNnzg8w8/6/2+F3AV9sZ9UJ8c2j5MWBdku9m8NfBpqp6IMnlDOZxmr3NY8zx/1dVvSXJXzMY178pyQur6u4F1iYtmGf4OhyclOS5bfkC4NPDK2twH4IvJjkfvn1f3ue01TcBP9+Wt4zxXjPh/uX2l8MvjLHNIwxuc0l7/5Or6o6qeiuwE/jhMV5DWjIDX4eD3cDWJLczuPfrFSP6vAq4MMnM7IIzt7C7BPitJLcwGOZ56FBvVFVfZTCv+h0MbqTymTHq+yRw6syHtsAl7QPfzzIYv5/rzlHSRDlbpta0DG4f+VftA9TFbP8U4BtVVUm2ABdU1Yrcz1RaaY7hq3c/DryrXVr5VQZzz0uHJc/wJakTjuFLUicMfEnqhIEvSZ0w8CWpEwa+JHXCwJekTvwfxt34wE9uaScAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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xR4AvAG8A944gnzQUTr9on1NV/wisB6bm7PsRsB24gcGqldJEcqSufU6Sk4H9GCyedNCch/4E+Luq+v5gAUtp8ljq2lfsmVOHwYdTbK6q9+aWd1U9h1e9aMK5SqMkNcQ5dUlqiKUuSQ2x1CWpIZa6JDXEUpekhljqktQQS12SGmKpS1JD/hcrTevRwP8/MQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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50bhrkUbBoJe6A3S+QHcQmNQcg15Htf5cSufTnS72ir7tGUk+0J9L/PYkdyS5rL/v5Un+tT8R2mfnDs2XJplBr6PdpXTncf8P4JEkZwG/C6wDfo3uaMbz4BfnXvoH4LKqejlwHXDNOIqWlmJkPw4uTYnNdCdmg+7Q9M3AM4FPVdXPge8luau//3TgTODO7vQlHEN3Clppohn0Omr151C5EDgzSdEFd/HEOXCe8hDg/qo67wiVKA2FUzc6ml0GfKyqXlBV66pqLd0vNT0M/F4/V38ycEHffw8wk+QXUzlJXjKOwqWlMOh1NNvMU7feb6L7oYhZutMWf5DujKmPVtXP6P44vCfJV4F76c4xLk00z14pLSDJc6rqx/30zt10v0b1vXHXJS2Hc/TSwm7vfyzmWcBfG/KaZm7RS1LjnKOXpMYZ9JLUOINekhpn0EtS4wx6SWqcQS9Jjfs/BtPpG+2qagEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for feature in train.columns:\n",
    "    sns.distplot(train[feature],kde=False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         0\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#数据探索\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0,np.NaN)\n",
    "#将0，替换为NaN\n",
    "print(train.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>serum_insulin_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   serum_insulin  serum_insulin_Missing\n",
       "0            NaN                      1\n",
       "1            NaN                      1\n",
       "2            NaN                      1\n",
       "3           94.0                      0\n",
       "4          168.0                      0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对缺失值较多的特征，新增一个特征，表示是否为缺失值\n",
    "train['Triceps_skin_fold_thickness_Missing'] = train['Triceps_skin_fold_thickness'].apply(lambda x:1 if pd.isnull(x) else 0)\n",
    "#值为空和0的，都设置为1.\n",
    "train[['Triceps_skin_fold_thickness','Triceps_skin_fold_thickness_Missing']].head()\n",
    "\n",
    "train['serum_insulin_Missing'] = train['serum_insulin'].apply(lambda x:1 if pd.isnull(x) else 0)\n",
    "train[['serum_insulin','serum_insulin_Missing']].head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                       0\n",
      "Plasma_glucose_concentration    0\n",
      "blood_pressure                  0\n",
      "Triceps_skin_fold_thickness     0\n",
      "serum_insulin                   0\n",
      "BMI                             0\n",
      "Diabetes_pedigree_function      0\n",
      "Age                             0\n",
      "Target                          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对于缺失的多的Triceps_skin_fold_thickness和serum_insulin，新增一个特征来表示。\n",
    "#若缺失较少（或随机缺失），则取到各列的均值，然后将NaN替换为各个特征的均值。\n",
    "train.drop(['Triceps_skin_fold_thickness_Missing','serum_insulin_Missing'],axis=1,inplace = True)\n",
    "medians = train.median()\n",
    "train = train.fillna(medians)\n",
    "print(train.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.639947</td>\n",
       "      <td>0.866045</td>\n",
       "      <td>-0.031990</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>0.166619</td>\n",
       "      <td>0.468492</td>\n",
       "      <td>1.425995</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.205066</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-0.852200</td>\n",
       "      <td>-0.365061</td>\n",
       "      <td>-0.190672</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.233880</td>\n",
       "      <td>2.016662</td>\n",
       "      <td>-0.693761</td>\n",
       "      <td>-0.012301</td>\n",
       "      <td>-0.181541</td>\n",
       "      <td>-1.332500</td>\n",
       "      <td>0.604397</td>\n",
       "      <td>-0.105584</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.844885</td>\n",
       "      <td>-1.073567</td>\n",
       "      <td>-0.528319</td>\n",
       "      <td>-0.695245</td>\n",
       "      <td>-0.540642</td>\n",
       "      <td>-0.633881</td>\n",
       "      <td>-0.920763</td>\n",
       "      <td>-1.041549</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-1.141852</td>\n",
       "      <td>0.504422</td>\n",
       "      <td>-2.679076</td>\n",
       "      <td>0.670643</td>\n",
       "      <td>0.316566</td>\n",
       "      <td>1.549303</td>\n",
       "      <td>5.484909</td>\n",
       "      <td>-0.020496</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0   0.639947                      0.866045       -0.031990   \n",
       "1  -0.844885                     -1.205066       -0.528319   \n",
       "2   1.233880                      2.016662       -0.693761   \n",
       "3  -0.844885                     -1.073567       -0.528319   \n",
       "4  -1.141852                      0.504422       -2.679076   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin       BMI  \\\n",
       "0                     0.670643      -0.181541  0.166619   \n",
       "1                    -0.012301      -0.181541 -0.852200   \n",
       "2                    -0.012301      -0.181541 -1.332500   \n",
       "3                    -0.695245      -0.540642 -0.633881   \n",
       "4                     0.670643       0.316566  1.549303   \n",
       "\n",
       "   Diabetes_pedigree_function       Age  Target  \n",
       "0                    0.468492  1.425995       1  \n",
       "1                   -0.365061 -0.190672       0  \n",
       "2                    0.604397 -0.105584       1  \n",
       "3                   -0.920763 -1.041549       0  \n",
       "4                    5.484909 -0.020496       1  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据标准化\n",
    "y_train = train['Target']\n",
    "X_train = train.drop(['Target'],axis=1)\n",
    "#用于保存特征工程后的结果\n",
    "feat_names = X_train.columns\n",
    "\n",
    "#数据标准化\n",
    "X_train = ss.fit_transform(X_train)\n",
    "X_train = pd.DataFrame(columns = feat_names,data=X_train)\n",
    "train = pd.concat([X_train,y_train],axis=1)\n",
    "train.to_csv(path + 'FE_pima-indians-diabetes.csv',index = False,header=True)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is :  [0.48797856 0.53011593 0.4562292  0.422546   0.48392885]\n",
      "cv logloss is :  0.47615970944434044\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#模型训练\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "#交叉验证用于评估性能和参数调优（模型选择）\n",
    "#分类任务中交叉验证缺省时采用StratifiedKFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "loss = cross_val_score(lr,X_train,y_train,cv=5,scoring='neg_log_loss')\n",
    "#分为五折，每折的得分\n",
    "print ('logloss of each fold is : ', - loss)\n",
    "print('cv logloss is : ',-loss.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 14 candidates, totalling 70 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  10 tasks      | elapsed:    6.0s\n",
      "[Parallel(n_jobs=4)]: Done  48 out of  70 | elapsed:    6.1s remaining:    2.8s\n",
      "[Parallel(n_jobs=4)]: Done  70 out of  70 | elapsed:    6.2s finished\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=4,\n",
       "       param_grid={'penalty': ['l1', 'l2'], 'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_log_loss', verbose=5)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#正则化的LR 及参数调优。\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "penaltys = ['l1','l2']\n",
    "Cs = [0.001,0.01,0.1,1,10,100,1000]\n",
    "tuned_parameters = dict(penalty = penaltys,C = Cs)\n",
    "lr_penalty = LogisticRegression()\n",
    "grid = GridSearchCV(lr_penalty,tuned_parameters,cv = 5,scoring='neg_log_loss',n_jobs=4,verbose=5)\n",
    "grid.fit(X_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4760270752308448\n",
      "{'C': 1, 'penalty': 'l1'}\n"
     ]
    }
   ],
   "source": [
    "print(-grid.best_score_)\n",
    "print(grid.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>columns</th>\n",
       "      <th>coeffient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Plasma_glucose_concentration</td>\n",
       "      <td>[1.1284638749299336]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BMI</td>\n",
       "      <td>[0.629193266606157]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pregnants</td>\n",
       "      <td>[0.409606097775606]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Diabetes_pedigree_function</td>\n",
       "      <td>[0.2793183492247577]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Age</td>\n",
       "      <td>[0.14396154596090388]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Triceps_skin_fold_thickness</td>\n",
       "      <td>[0.024728260668064616]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>serum_insulin</td>\n",
       "      <td>[-0.0840774206151623]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>blood_pressure</td>\n",
       "      <td>[-0.09299774403538834]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        columns               coeffient\n",
       "1  Plasma_glucose_concentration    [1.1284638749299336]\n",
       "5                           BMI     [0.629193266606157]\n",
       "0                     pregnants     [0.409606097775606]\n",
       "6    Diabetes_pedigree_function    [0.2793183492247577]\n",
       "7                           Age   [0.14396154596090388]\n",
       "3   Triceps_skin_fold_thickness  [0.024728260668064616]\n",
       "4                 serum_insulin   [-0.0840774206151623]\n",
       "2                blood_pressure  [-0.09299774403538834]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\"columns\":list(feat_names),\"coeffient\":list(grid.best_estimator_.coef_.T)})\n",
    "df.sort_values(by=['coeffient'],ascending=False)#以系数排序，降序\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.69314718 0.62652347 0.63462991 0.50971499 0.4698818  0.46565315\n",
      " 0.46285727 0.46277622 0.46273091 0.46273001 0.46272953 0.46272951\n",
      " 0.46272951 0.46272951]\n",
      "[[0.69314718 0.62652347]\n",
      " [0.63462991 0.50971499]\n",
      " [0.4698818  0.46565315]\n",
      " [0.46285727 0.46277622]\n",
      " [0.46273091 0.46273001]\n",
      " [0.46272953 0.46272951]\n",
      " [0.46272951 0.46272951]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot CV误差曲线\n",
    "test_means = -grid.cv_results_['mean_test_score']\n",
    "test_stds = grid.cv_results_['std_test_score']\n",
    "train_means = -grid.cv_results_['mean_train_score']\n",
    "train_stds = grid.cv_results_['std_train_score']\n",
    "print(train_means)\n",
    "\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "print(train_scores)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i,value in enumerate(penaltys):\n",
    "    plt.errorbar(x_axis,test_scores[:,i],yerr=test_stds[:,i],label=penaltys[i]+'Test_stds')\n",
    "    plt.errorbar(x_axis,train_scores[:,i],yerr=train_stds[:,i],label=penaltys[i]+'Train_stds')\n",
    "plt.legend()\n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('logloss')\n",
    "plt.savefig(path+'LogisticGridSearchCV_c.png')#savefig:save picture\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7747395833333334\n",
      "{'C': 0.1, 'penalty': 'l2'}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n",
      "C:\\Users\\79021\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "#缺省scoring为正确率\n",
    "grid = GridSearchCV(lr_penalty,tuned_parameters,cv=5)\n",
    "grid.fit(X_train,y_train)\n",
    "print(grid.best_score_)\n",
    "print(grid.best_params_)\n",
    "\n",
    "df = pd.DataFrame({\"columns\":list(feat_names),\"coeffient\":list(grid.best_estimator_.coef_.T)})\n",
    "df.sort_values(by=['coeffient'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.65104208 -0.74674929 -0.70703318 -0.76400042 -0.76985832 -0.77311353\n",
      " -0.77473955 -0.77376341 -0.77474061 -0.77441487 -0.77441487 -0.77441487\n",
      " -0.77441487 -0.77441487]\n",
      "[0.00051895 0.00786489 0.00543745 0.00596881 0.00641678 0.00658318\n",
      " 0.00530451 0.00434145 0.00413207 0.0041437  0.0041437  0.0041437\n",
      " 0.0041437  0.0041437 ]\n",
      "[[-0.65104208 -0.74674929]\n",
      " [-0.70703318 -0.76400042]\n",
      " [-0.76985832 -0.77311353]\n",
      " [-0.77473955 -0.77376341]\n",
      " [-0.77474061 -0.77441487]\n",
      " [-0.77441487 -0.77441487]\n",
      " [-0.77441487 -0.77441487]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot CV误差曲线\n",
    "test_means = -grid.cv_results_['mean_test_score']\n",
    "test_stds = grid.cv_results_['std_test_score']\n",
    "train_means = -grid.cv_results_['mean_train_score']\n",
    "train_stds = grid.cv_results_['std_train_score']\n",
    "print(train_means)\n",
    "print(train_stds)\n",
    "n_Cs = len(Cs)\n",
    "number_penaltys = len(penaltys)\n",
    "test_scores = np.array(test_means).reshape(n_Cs,number_penaltys)\n",
    "train_scores = np.array(train_means).reshape(n_Cs,number_penaltys)\n",
    "print(train_scores)\n",
    "test_stds = np.array(test_stds).reshape(n_Cs,number_penaltys)\n",
    "train_stds = np.array(train_stds).reshape(n_Cs,number_penaltys)\n",
    "\n",
    "x_axis = np.log10(Cs)\n",
    "for i,value in enumerate(penaltys):\n",
    "    plt.errorbar(x_axis,test_scores[:,i],yerr=test_stds[:,i],label=penaltys[i]+'Test_stds')\n",
    "    plt.errorbar(x_axis,train_scores[:,i],yerr=train_stds[:,i],label=penaltys[i]+'Train_stds')\n",
    "plt.legend()\n",
    "plt.xlabel('log(C)')\n",
    "plt.ylabel('logloss')\n",
    "plt.savefig(path+'LogisticGridSearchCV_accuracy.png')#savefig:save picture\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
